Attentive Weights Generation for Few Shot Learning via Information Maximization
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Publisher
IEEE
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http://xplorestaging.ieee.org/ielx7/9142308/9156271/09157006.pdf?arnumber=9157006
Cited by 66 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Multi-view cognition with path search for one-shot part labeling;Computer Vision and Image Understanding;2024-07
2. Multimodal Prototype Enhanced Few-Shot Image Recognition with Dense Attention-Weighted Knowledge Graph Propagation;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17
3. Improved Continually Evolved Classifiers for Few-Shot Class-Incremental Learning;IEEE Transactions on Circuits and Systems for Video Technology;2024-02
4. Self-Supervison with data-augmentation improves few-shot learning;Applied Intelligence;2024-02
5. Few-shot classification based on manifold metric learning;Journal of Electronic Imaging;2024-01-24
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